The Airport With the Most Traffic - Practice Coding Problems

The Airport With the Most Traffic - Problem

Database Medium

🛫 The Airport With the Most Traffic

You're working as a data analyst for a major aviation authority, and you need to identify which airports handle the most flight traffic. Given a database table containing flight route information, your task is to find the airport(s) with the highest total traffic.

The Problem: Calculate the total number of flights for each airport by counting both departing and arriving flights. An airport's traffic score is the sum of all flights that either departed from or arrived at that specific airport.

Input: A Flights table with three columns:

departure_airport (int): ID of the departure airport
arrival_airport (int): ID of the arrival airport
flights_count (int): Number of flights on this route

Output: Return the airport ID(s) with the maximum total traffic. If multiple airports tie for the highest traffic, return all of them.

Key Insight: Each flight route contributes to the traffic count of two airports - the departure airport and the arrival airport!

Input & Output

example_1.sql — Basic Traffic Analysis

$ Input: Flights table: | departure_airport | arrival_airport | flights_count | |-------------------|-----------------|---------------| | 1 | 2 | 15 | | 2 | 1 | 10 | | 2 | 3 | 25 | | 3 | 2 | 5 |

› Output: | airport_id | |------------| | 2 |

💡 Note: Airport 1: 15 (departure) + 10 (arrival) = 25 total flights. Airport 2: 10 + 25 (departures) + 15 + 5 (arrivals) = 55 total flights. Airport 3: 25 (departure) + 5 (arrival) = 30 total flights. Airport 2 has the highest traffic with 55 flights.

example_2.sql — Tied Airports

$ Input: Flights table: | departure_airport | arrival_airport | flights_count | |-------------------|-----------------|---------------| | 1 | 3 | 20 | | 2 | 3 | 20 | | 3 | 1 | 10 | | 3 | 2 | 10 |

› Output: | airport_id | |------------| | 1 | | 2 | | 3 |

💡 Note: Airport 1: 20 (departure) + 10 (arrival) = 30 total flights. Airport 2: 20 (departure) + 10 (arrival) = 30 total flights. Airport 3: 10 + 10 (departures) + 20 + 20 (arrivals) = 60 total flights. Wait, let me recalculate: All airports have 30 flights each, so all three are returned as they tie for maximum traffic.

example_3.sql — Single Route

$ Input: Flights table: | departure_airport | arrival_airport | flights_count | |-------------------|-----------------|---------------| | 5 | 7 | 100 |

› Output: | airport_id | |------------| | 5 | | 7 |

💡 Note: Only one flight route exists. Airport 5 has 100 departure flights and Airport 7 has 100 arrival flights. Both airports tie with 100 total flights each, so both are returned.

Constraints

1 ≤ Number of rows in Flights ≤ 10⁴
1 ≤ departure_airport, arrival_airport ≤ 10³
1 ≤ flights_count ≤ 10⁴
departure_airport ≠ arrival_airport (no self-loops)
The combination (departure_airport, arrival_airport) is unique for each row

Visualization

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Understanding the Visualization

Identify Traffic Sources

Each flight record represents traffic flowing between two airports

Split Traffic Streams

Use UNION ALL to create separate records for departure and arrival traffic

Aggregate by Airport

GROUP BY airport_id to sum all traffic for each airport

Find Peak Traffic

Identify airport(s) with maximum total traffic

Key Takeaway

🎯 Key Insight: Using UNION ALL to combine departure and arrival streams allows for efficient single-pass aggregation, achieving optimal O(n) performance while correctly counting each flight's contribution to both airports involved.

Asked in

a Amazon 45 G Google 38 ⊞ Microsoft 32 f Meta 28

The optimal approach uses UNION ALL to combine departure and arrival data streams, then applies GROUP BY for efficient aggregation. This one-pass solution achieves O(n) time complexity by scanning the data only once, making it significantly faster than nested subquery approaches for large datasets.

Common Approaches

Approach	Time	Space	Notes
✓ One-Pass Aggregation (Optimal)	O(n)	O(k)	Calculate all airport traffic counts in a single efficient query using UNION ALL and GROUP BY
Brute Force Subquery Approach	O(n²)	O(n)	Calculate traffic for each airport using separate subqueries

One-Pass Aggregation (Optimal) — Algorithm Steps

Use UNION ALL to combine departure and arrival airport data into one unified stream
Each flight record contributes its count to both departure and arrival airports
GROUP BY airport_id to aggregate total traffic for each airport
Use window function or subquery to find the maximum traffic
Filter results to return only airports with maximum traffic

Visualization

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Step-by-Step Walkthrough

UNION ALL Combination

Merge departure and arrival data into single stream

Group By Airport

Aggregate flight counts for each airport

Find Maximum

Identify highest traffic value

Filter Results

Return airports with maximum traffic

Code -

solution.c — C

// Optimal One-Pass SQL Solution
#include <stdio.h>

const char* getOptimalSolution() {
    return "\
        WITH airport_traffic AS (\
            SELECT \
                airport_id,\
                SUM(flights_count) as total_traffic\
            FROM (\
                SELECT departure_airport as airport_id, flights_count\
                FROM Flights\
                UNION ALL\
                SELECT arrival_airport as airport_id, flights_count\
                FROM Flights\
            ) combined_traffic\
            GROUP BY airport_id\
        ),\
        max_traffic AS (\
            SELECT MAX(total_traffic) as max_traffic\
            FROM airport_traffic\
        )\
        SELECT airport_id\
        FROM airport_traffic\
        WHERE total_traffic = (SELECT max_traffic FROM max_traffic);\
    ";
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Single pass through the data with efficient grouping and aggregation

✓ Linear Growth

Space Complexity

O(k)

Space proportional to the number of unique airports (k), which is typically much smaller than the number of flight records (n)

✓ Linear Space

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High Frequency

~15 min Avg. Time

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🛫 The Airport With the Most Traffic

Input & Output

Constraints

Visualization

Related Problems

Common Approaches

One-Pass Aggregation (Optimal) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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